# Blog tags

- significance 15
- power 16
- simplicity 14
- R 51
- breakpoint regression 3
- multiple comparisons 5
- silly tests 6
- graphics 21
- effect sizes 11
- measurement error 8
- design features 11
- organisation 5
- logistic regression 7
- correlational studies 7
- mixed-effects models 11
- cluster-randomised experiments 6
- generalised additive models 3
- non-linearities 9
- missing data 1
- open science 3
- correction 1
- multiple regression 9
- reading 2
- tutorial 10
- machine learning 1
- random forests 1
- R tip 1
- bootstrapping 2
- replication 2
- predictive modelling 2
- assumptions 5
- research questions 4
- Bayesian statistics 7
- data entry 1
- preprint 2
- cannonball 2
- brms 3
- contrast coding 3
- walkthrough 1
- collinearity 1
- Stan 2
- reliability 2
- nonparametric tests 1
- research design 1
- teaching materials 1
- DAG 1

## significance

- Capitalising on covariates in cluster-randomised experiments
- Nonparametric tests aren't a silver bullet when parametric assumptions are violated
- Adjusting for a covariate in cluster-randomised experiments
- Walkthrough: A significance test for a two-group comparison
- Confidence interval-based optional stopping
- The Centre for Open Science's Preregistration Challenge: Why it's relevant and some recommended background reading
- On correcting for multiple comparisons: Five scenarios
- Experiments with intact groups: spurious significance with improperly weighted t-tests
- Analysing experiments with intact groups: the problem and an easy solution
- Power simulations for comparing independent correlations
- Explaining key concepts using permutation tests
- Assessing differences of significance
- A purely graphical explanation of p-values
- Calibrating p-values in 'flexible' piecewise regression models
- Analysing pretest/posttest data

## power

- Capitalising on covariates in cluster-randomised experiments
- Nonparametric tests aren't a silver bullet when parametric assumptions are violated
- Adjusting for a covariate in cluster-randomised experiments
- Increasing power and precision using covariates
- Abandoning standardised effect sizes and opening up other roads to power
- On correcting for multiple comparisons: Five scenarios
- Causes and consequences of unequal sample sizes
- The problem with cutting up continuous variables and what to do when things aren't linear
- Analysing experiments with intact groups: the problem and an easy solution
- Covariate adjustment in logistic mixed models: Is it worth the effort?
- Covariate adjustment in logistic regression — and some counterintuitive findings
- Silly significance tests: Tests unrelated to the genuine research questions
- Power simulations for comparing independent correlations
- More on why I don't like standardised effect sizes
- The curious neglect of covariates in discussions of statistical power
- Analysing pretest/posttest data

## simplicity

- In research, don't do things you don't see the point of
- Nonparametric tests aren't a silver bullet when parametric assumptions are violated
- Five suggestions for simplifying research reports
- Adjusting for a covariate in cluster-randomised experiments
- Before worrying about model assumptions, think about model relevance
- Common-language effect sizes
- Surviving the ANOVA onslaught
- Silly significance tests: The main effects no one is interested in
- Silly significance tests: Tests unrelated to the genuine research questions
- A more selective approach to reporting statistics
- Overaccuracy and false precision
- Silly significance tests: Tautological tests
- Silly significance tests: Balance tests
- Analysing pretest/posttest data

## R

- An R function for computing Levenshtein distances between texts using the word as the unit of comparison
- The consequences of controlling for a post-treatment variable
- Capitalising on covariates in cluster-randomised experiments
- Tutorial: Visualising statistical uncertainty using model-based graphs
- Nonparametric tests aren't a silver bullet when parametric assumptions are violated
- Tutorial: Obtaining directly interpretable regression coefficients by recoding categorical predictors
- Baby steps in Bayes: Incorporating reliability estimates in regression models
- Baby steps in Bayes: Accounting for measurement error on a control variable
- Drawing scatterplot matrices
- Adjusting for a covariate in cluster-randomised experiments
- Collinearity isn't a disease that needs curing
- Interactions in logistic regression models
- Walkthrough: A significance test for a two-group comparison
- Guarantees in the long run vs. interpreting the data at hand: Two analyses of clustered data
- Baby steps in Bayes: Recoding predictors and homing in on specific comparisons
- Introducing cannonball - Tools for teaching statistics
- Looking for comments on a paper on model assumptions
- Checking model assumptions without getting paranoid
- Confidence interval-based optional stopping
- Creating comparable sets of stimuli
- Interactions between continuous variables
- Tutorial: Adding confidence bands to effect displays
- Tutorial: Plotting regression models
- Confidence intervals for standardised mean differences
- Some illustrations of bootstrapping
- What data patterns can lie behind a correlation coefficient?
- Common-language effect sizes
- Tutorial: Drawing a dot plot
- R tip: Ordering factor levels more easily
- Classifying second-language learners as native- or non-nativelike: Don't neglect classification error rates
- Tutorial: Drawing a boxplot
- Tutorial: Drawing a line chart
- Tutorial: Drawing a scatterplot
- Why reported R² values are often too high
- Experiments with intact groups: spurious significance with improperly weighted t-tests
- Drawing a scatterplot with a non-linear trend line
- Causes and consequences of unequal sample sizes
- The problem with cutting up continuous variables and what to do when things aren't linear
- Analysing experiments with intact groups: the problem and an easy solution
- Covariate adjustment in logistic mixed models: Is it worth the effort?
- Covariate adjustment in logistic regression — and some counterintuitive findings
- Some tips on preparing your data for analysis
- Power simulations for comparing independent correlations
- Explaining key concepts using permutation tests
- Thinking about graphs
- Some alternatives to bar plots
- Assessing differences of significance
- Silly significance tests: Balance tests
- A purely graphical explanation of p-values
- Calibrating p-values in 'flexible' piecewise regression models
- Analysing pretest/posttest data

## breakpoint regression

- Baby steps in Bayes: Piecewise regression with two breakpoints
- Baby steps in Bayes: Piecewise regression
- Calibrating p-values in 'flexible' piecewise regression models

## multiple comparisons

- The Centre for Open Science's Preregistration Challenge: Why it's relevant and some recommended background reading
- Why reported R² values are often too high
- On correcting for multiple comparisons: Five scenarios
- Silly significance tests: Tests unrelated to the genuine research questions
- Calibrating p-values in 'flexible' piecewise regression models

## silly tests

- In research, don't do things you don't see the point of
- Five suggestions for simplifying research reports
- Silly significance tests: The main effects no one is interested in
- Silly significance tests: Tests unrelated to the genuine research questions
- Silly significance tests: Tautological tests
- Silly significance tests: Balance tests

## graphics

- Tutorial: Visualising statistical uncertainty using model-based graphs
- Five suggestions for simplifying research reports
- Drawing scatterplot matrices
- Before worrying about model assumptions, think about model relevance
- Baby steps in Bayes: Recoding predictors and homing in on specific comparisons
- A closer look at a classic study (Bailey et al. 1974)
- Looking for comments on a paper on model assumptions
- Checking model assumptions without getting paranoid
- Interactions between continuous variables
- Tutorial: Adding confidence bands to effect displays
- Tutorial: Plotting regression models
- What data patterns can lie behind a correlation coefficient?
- Tutorial: Drawing a dot plot
- R tip: Ordering factor levels more easily
- Tutorial: Drawing a boxplot
- Tutorial: Drawing a line chart
- Tutorial: Drawing a scatterplot
- Drawing a scatterplot with a non-linear trend line
- A more selective approach to reporting statistics
- Thinking about graphs
- Some alternatives to bar plots

## effect sizes

- Abandoning standardised effect sizes and opening up other roads to power
- Confidence intervals for standardised mean differences
- Which predictor is most important? Predictive utility vs. construct importance
- What data patterns can lie behind a correlation coefficient?
- Common-language effect sizes
- Why reported R² values are often too high
- Covariate adjustment in logistic mixed models: Is it worth the effort?
- Covariate adjustment in logistic regression — and some counterintuitive findings
- More on why I don't like standardised effect sizes
- A more selective approach to reporting statistics
- Why I don't like standardised effect sizes

## measurement error

- Interpreting regression models: a reading list
- Baby steps in Bayes: Incorporating reliability estimates in regression models
- Baby steps in Bayes: Accounting for measurement error on a control variable
- Abandoning standardised effect sizes and opening up other roads to power
- Which predictor is most important? Predictive utility vs. construct importance
- Controlling for confounding variables in correlational research: Four caveats
- More on why I don't like standardised effect sizes
- Why I don't like standardised effect sizes

## design features

- Capitalising on covariates in cluster-randomised experiments
- Consider generalisability
- Suggestions for more informative replication studies
- Increasing power and precision using covariates
- Confidence interval-based optional stopping
- Creating comparable sets of stimuli
- Abandoning standardised effect sizes and opening up other roads to power
- Experiments with intact groups: spurious significance with improperly weighted t-tests
- Causes and consequences of unequal sample sizes
- Analysing experiments with intact groups: the problem and an easy solution
- Explaining key concepts using permutation tests

## organisation

- Interpreting regression models: a reading list
- Creating comparable sets of stimuli
- Automatise repetitive tasks
- The Centre for Open Science's Preregistration Challenge: Why it's relevant and some recommended background reading
- Some tips on preparing your data for analysis

## logistic regression

- Tutorial: Visualising statistical uncertainty using model-based graphs
- Interpreting regression models: a reading list
- Interactions in logistic regression models
- Tutorial: Adding confidence bands to effect displays
- Tutorial: Plotting regression models
- Covariate adjustment in logistic mixed models: Is it worth the effort?
- Covariate adjustment in logistic regression — and some counterintuitive findings

## correlational studies

- Interpreting regression models: a reading list
- Baby steps in Bayes: Incorporating reliability estimates in regression models
- Baby steps in Bayes: Accounting for measurement error on a control variable
- Drawing scatterplot matrices
- Which predictor is most important? Predictive utility vs. construct importance
- What data patterns can lie behind a correlation coefficient?
- Controlling for confounding variables in correlational research: Four caveats

## mixed-effects models

- Tutorial: Visualising statistical uncertainty using model-based graphs
- Interpreting regression models: a reading list
- Tutorial: Obtaining directly interpretable regression coefficients by recoding categorical predictors
- Adjusting for a covariate in cluster-randomised experiments
- Guarantees in the long run vs. interpreting the data at hand: Two analyses of clustered data
- Baby steps in Bayes: Recoding predictors and homing in on specific comparisons
- Consider generalisability
- Suggestions for more informative replication studies
- Tutorial: Adding confidence bands to effect displays
- Tutorial: Plotting regression models
- Covariate adjustment in logistic mixed models: Is it worth the effort?

## cluster-randomised experiments

- Capitalising on covariates in cluster-randomised experiments
- Five suggestions for simplifying research reports
- Adjusting for a covariate in cluster-randomised experiments
- Guarantees in the long run vs. interpreting the data at hand: Two analyses of clustered data
- Experiments with intact groups: spurious significance with improperly weighted t-tests
- Analysing experiments with intact groups: the problem and an easy solution

## generalised additive models

- Increasing power and precision using covariates
- Interactions between continuous variables
- The problem with cutting up continuous variables and what to do when things aren't linear

## non-linearities

- Drawing scatterplot matrices
- Before worrying about model assumptions, think about model relevance
- Baby steps in Bayes: Piecewise regression with two breakpoints
- Baby steps in Bayes: Piecewise regression
- Increasing power and precision using covariates
- Interactions between continuous variables
- What data patterns can lie behind a correlation coefficient?
- Drawing a scatterplot with a non-linear trend line
- The problem with cutting up continuous variables and what to do when things aren't linear

## missing data

## open science

- Five suggestions for simplifying research reports
- Some advantages of sharing your data and code

## correction

## multiple regression

- The consequences of controlling for a post-treatment variable
- Tutorial: Visualising statistical uncertainty using model-based graphs
- Interpreting regression models: a reading list
- Tutorial: Obtaining directly interpretable regression coefficients by recoding categorical predictors
- Drawing scatterplot matrices
- Collinearity isn't a disease that needs curing
- Tutorial: Adding confidence bands to effect displays
- Tutorial: Plotting regression models
- Why reported R² values are often too high

## reading

## tutorial

- Interactions in logistic regression models
- Looking for comments on a paper on model assumptions
- Checking model assumptions without getting paranoid
- Tutorial: Adding confidence bands to effect displays
- Tutorial: Plotting regression models
- Tutorial: Drawing a dot plot
- Tutorial: Drawing a boxplot
- Tutorial: Drawing a line chart
- Tutorial: Drawing a scatterplot

## machine learning

## random forests

## R tip

## bootstrapping

## replication

- Suggestions for more informative replication studies
- Draft: Replication success as predictive utility

## predictive modelling

## assumptions

- Nonparametric tests aren't a silver bullet when parametric assumptions are violated
- Collinearity isn't a disease that needs curing
- Before worrying about model assumptions, think about model relevance
- Looking for comments on a paper on model assumptions
- Checking model assumptions without getting paranoid

## research questions

- In research, don't do things you don't see the point of
- Interpreting regression models: a reading list
- A brief comment on research questions

## Bayesian statistics

- Tutorial: Visualising statistical uncertainty using model-based graphs
- Baby steps in Bayes: Incorporating reliability estimates in regression models
- Baby steps in Bayes: Accounting for measurement error on a control variable
- Interactions in logistic regression models
- Baby steps in Bayes: Recoding predictors and homing in on specific comparisons
- Baby steps in Bayes: Piecewise regression with two breakpoints
- Baby steps in Bayes: Piecewise regression

## data entry

## preprint

- Capitalising on covariates in cluster-randomised experiments
- Looking for comments on a paper on model assumptions

## cannonball

- Introducing cannonball - Tools for teaching statistics
- Looking for comments on a paper on model assumptions

## brms

- Tutorial: Visualising statistical uncertainty using model-based graphs
- Interactions in logistic regression models
- Baby steps in Bayes: Recoding predictors and homing in on specific comparisons

## contrast coding

- Interpreting regression models: a reading list
- Baby steps in Bayes: Recoding predictors and homing in on specific comparisons

## walkthrough

## collinearity

## Stan

- Baby steps in Bayes: Incorporating reliability estimates in regression models
- Baby steps in Bayes: Accounting for measurement error on a control variable

## reliability

- Interpreting regression models: a reading list
- Baby steps in Bayes: Incorporating reliability estimates in regression models